Pinero de Plaza Maria Alejandra, Lambrakis Kristina, Marmolejo-Ramos Fernando, Beleigoli Alline, Archibald Mandy, Yadav Lalit, McMillan Penelope, Clark Robyn, Lawless Michael, Morton Erin, Hendriks Jeroen, Kitson Alison, Visvanathan Renuka, Chew Derek P, Barrera Causil Carlos Javier
Caring Futures Institute, Flinders University, Adelaide, South, Australia.
Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia; MonashHeart, Monash Health, Melbourne, Victoria, Australia; College of Medicine and Public Health, Flinders University, Adelaide, South, Australia.
Int J Med Inform. 2025 Apr;196:105810. doi: 10.1016/j.ijmedinf.2025.105810. Epub 2025 Jan 28.
Chest pain diagnosis in emergency care is hindered by overlapping cardiac and non-cardiac symptoms, causing diagnostic uncertainty. Artificial Intelligence, such as RAPIDx AI, aims to enhance accuracy through clinical and biochemical data integration, but its adoption relies on addressing usability, explainability, and seamless workflow integration without disrupting care.
Evaluate RAPIDx AI's integration into clinical workflows, address usability barriers, and optimise its adoption in emergencies.
The PROLIFERATE_AI framework was implemented across 12 EDs (July 2022-January 2024) with 39 participants: 15 experts co-designed a survey via Expert Knowledge Elicitation (EKE), applied to 24 ED clinicians to assess RAPIDx AI usability and adoption. Bayesian inference, using priors, estimated comprehension, emotional engagement, usage, and preference, while Monte Carlo simulations quantified uncertainty and variability, generating posterior means and 95% bootstrapped confidence intervals. Qualitative thematic analysis identified barriers and optimisation needs, with data triangulated through the PROLIFERATE_AI scoring system to rate RAPIDx AI's performance by user roles and demographics.
Registrars exhibited the highest comprehension (median: 0.466, 95 % CI: 0.41-0.51) and preference (median: 0.458, 95 % CI: 0.41-0.48), while residents/interns scored the lowest in comprehension (median: 0.198, 95 % CI: 0.17-0.26) and emotional engagement (median: 0.112, 95 % CI: 0.09-0.14). Registered nurses showed strong emotional engagement (median: 0.379, 95 % CI: 0.35-0.45). Novice users faced usability and workflow integration barriers, while experienced clinicians suggested automation and streamlined workflows. RAPIDx AI scored "Good Impact," excelling with trained users but requiring targeted refinements for novices.
RAPIDx AI enhances diagnostic accuracy and efficiency for experienced users, but usability challenges for novices highlight the need for targeted training and interface refinements. The PROLIFERATE_AI framework offers a robust methodology for evaluating and scaling AI solutions, addressing the evolving needs of sociotechnical systems.
急诊护理中的胸痛诊断因心脏和非心脏症状重叠而受阻，导致诊断存在不确定性。人工智能，如RAPIDx AI，旨在通过整合临床和生化数据提高准确性，但其应用依赖于解决可用性、可解释性以及在不干扰护理的情况下实现无缝工作流程整合。
评估RAPIDx AI在临床工作流程中的整合情况，解决可用性障碍，并优化其在急诊中的应用。
在2022年7月至2024年1月期间，在12个急诊科实施了PROLIFERATE_AI框架，共有39名参与者：15名专家通过专家知识启发法（EKE）共同设计了一项调查，并应用于24名急诊科临床医生，以评估RAPIDx AI的可用性和采用情况。使用先验的贝叶斯推理估计理解度、情感参与度、使用率和偏好，同时蒙特卡罗模拟量化不确定性和变异性，生成后验均值和95%的自抽样置信区间。定性主题分析确定了障碍和优化需求，并通过PROLIFERATE_AI评分系统对数据进行三角测量，以按用户角色和人口统计学对RAPIDx AI的性能进行评分。
住院医生的理解度最高（中位数：0.466，95%置信区间：0.41 - 0.51）和偏好度最高（中位数：0.458，95%置信区间：0.41 - 0.48），而住院医师/实习医生的理解度最低（中位数：0.198，95%置信区间：0.17 - 0.26）和情感参与度最低（中位数：0.112，95%置信区间：0.09 - 0.14）。注册护士表现出较强的情感参与度（中位数：0.379，95%置信区间：0.35 - 0.45）。新手用户面临可用性和工作流程整合障碍，而经验丰富的临床医生建议实现自动化和简化工作流程。RAPIDx AI的评分为“良好影响”，在训练有素的用户中表现出色，但新手需要针对性的改进。
RAPIDx AI提高了经验丰富用户的诊断准确性和效率，但新手面临的可用性挑战凸显了针对性培训和界面改进的必要性。PROLIFERATE_AI框架为评估和扩展人工智能解决方案提供了一种强大的方法，可满足社会技术系统不断变化的需求。
